22 papers · ranked by Valyu relevance
Eran Agmon, Ryan K Spangler
Building multiscale biological models requires the integration of independently developed submodels, which means moving shared variables between them and coordinating when each submodel runs. Existing tools typically address isolated biological mechanisms with specific numerical methods, rarely specify which variables…
Robin Umbra, Ulrike Fasbender
This manuscript introduces the Interaction Discrepancy Model (IDM), a theoretical framework designed to enhance our understanding of person-environment interactions. Traditional models often overlook the dynamic, iterative, and feedback-driven nature of these interactions, typically focusing on episodic and isolated…
Dan Vasilescu, James C. Schaff, Ion I. Moraru, Michael L. Blinov
Mechanistic modeling in biology aims to describe biological processes based on details on molecular mechanisms and interactions. Rule-based mechanistic modeling enables the simulation of biological systems while explicitly accounting for molecular details, such as protein domains and their specific interactions.…
Yuya Okadome, Yazan Alkatshah, Yutaka Nakamura, David Mayerich
As expectations for computer graphic (CG) avatars and conversational robots increase, enhancing dialogue skills via multimodal channels is crucial for achieving fluent interactions with humans. Thus, automatic interaction motion generation is essential for autonomous conversation systems. Natural motion generation…
Alberto Ronzoni, Antony, Anina, M.P. Anjana + 7 more
The academic evolution of process mining is moving toward object centric process mining, marking a significant shift in how processes are modeled and analyzed. IBM has developed its own distinctive approach called Multilevel Process Mining. This paper provides a description of the two approaches and presents a…
Dan Liu, Francesca Young, Kieran D. Lamb, Adalberto Claudio Quiros + 5 more
Computational prediction of protein structure from amino acid sequence alone has been achieved with unprecedented accuracy, yet the prediction of protein-protein interactions remains a challenge. Here, we assess the ability of protein language models (PLMs), routinely applied to protein folding, to be retrained for…
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Background: Batch reactor process optimization has traditionally relied on Analysis of Variance (ANOVA) for factor effect quantification. However, Structural Equation Modeling (SEM) and machine learning (ML) offer complementary mechanistic and predictive capabilities that remain underexplored in chemical engineering…
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Pharmacophores are widely used to describe protein-ligand interactions, and the Grids of Pharmacophore Interaction Fields (GRAIL) method extends this concept by representing binding pockets as interpretable sets of interaction type-specific pharmacophoric maps. In this work, we propose a hybrid framework for binding…
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High-entropy layered double hydroxides (HE-LDHs) have shown great potential in oxygen evolution reaction (OER) catalysis due to their tunable compositions and electronic structures. However, the synergistic effects between multiple vacancies, such as metal and oxygen vacancies, remain poorly understood and challenging…
Shaoxian Li, Debin Zeng, Xiaoxi Dong, Yirong He + 7 more
A central objective in neuroscience is to elucidate how the brain generates complex dynamic activity through the interactions of brain areas. In this study, we utilized Interaction Network, a graph neural network model, to develop a computational framework for predicting whole-brain cortical blood oxygenation level…
Ishaan Kaushal, Amaresh Chakrabarti
This paper presents the Unified Smart Factory Model (USFM), a comprehensive framework designed to translate high-level sustainability goals into measurable factory-level indicators with a systematic information map of manufacturing activities. The manufacturing activities were modelled as set of manufacturing, assembly…
Mengjie Fan, Liang Zhou
—We introduce a design study process model for medical visualization based on the analysis of existing medical visualization and visual analysis works, and our own interdisciplinary research experience. With a literature review of related works covering various data types and applications, we identify features of…
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We present the next generation of AMP, a neural network potential (NNP) with anisotropic message passing designed to study large biomolecular systems at DFT accuracy in the condensed phase using a multiscale approach similar to quantum-mechanics/molecular-mechanics (QM/MM) with electrostatic embedding. We trained AMPv3…
Tao Tang, Taiguang Shen, Weizhuo Li, Yangyang Chen + 4 more
Protein-protein interactions (PPIs) are governed by two fundamental interfacial mechanisms: similarity-driven, often involving symmetric structural motifs, and complementarity-driven, arising from geometric and physicochemical matching between binding surfaces. Despite their biological significance, computational…
Md. Shahidul Islam, Md. Muhtasim Rahman Mim, Md. Raihan Kabir
Protein–protein interactions (PPIs) form the backbone of most cellular processes, governing signal transduction, gene regulation, and metabolic control. However, experimental approaches to identifying PPIs remain expensive, laborious, and often incomplete. Recent advances in protein language models (PLMs) have…
Giorgio Nicoletti, Antonio Celani
Understanding how living organisms process sensory information from their surroundings and translate it into decisions is a fundamental problem across biological scales – from biochemical signalling in single-cells to neural computations in animal brains. In this work, we address this challenge by introducing a method…
Paloma Marín Martínez, Sergio Ardanza-Trevijano, Javier Sabio, Juan E. Trinidad-Segovia
The digitalization of financial markets has shifted trading from voice to electronic channels, with Multi-Dealer-to-Client (MD2C) platforms now enabling clients to request quotes (RfQs) for financial instruments like bonds from multiple dealers simultaneously. In this competitive landscape, dealers cannot see each…
Daniel Amyot
Given the increasing amount of data available in organizational systems, there is an opportunity for early requirements engineering (RE) activities to be better based on evidence than ever before. Process mining (PM) has been used for over two decades to discover and analyze as-is process models from event logs…
Adithya Gungi, Pradyumna Sepúlveda Delgado, Ines F. Aitsahalia, Marta Blanco-Pozo + 1 more
Flexible, goal-directed behavior depends on learning predictive relationships, yet how reward shapes learned transition structure remains incompletely understood. Here we introduce the Sparse Cognitive Graph, a reinforcement-learning framework in which a continuously updated transition representation is sparsified into…
Nataliia Klievtsova, Juergen Mangler, Stefanie Rinderle-Ma
The artefact at the intersection of knowledge and process management is the process, which describes how enterprises are generating value. In knowledge management literature the relation of knowledge and processes is discussed, often leading to the definition of knowledge intensive processes, which entail a high level…
Madeline Jarvis-Cross, Andrew W. Bateman, Cole B. Brookson, Nicole Mideo + 1 more
Despite the impacts of within-host disease dynamics on disease outcomes in individual hosts and disease spread among-hosts, generic models of within-host population dynamics have received far less attention than their among-host counterparts. While a number of models have been proposed to explore theoretical…
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Computational modeling of enzymes provides molecular-level insight into catalysis, but the preparation of quantum mechanical (QM) calculations starting from experimental structures is a significant bottleneck for high-throughput studies. Automated tools developed to accelerate this process may fail to generalize across…